Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

• Published in: Energies (Basel) Vol. 14; no. 22; p. 7714
• Main Authors: Man, Ha Quang, Hien, Doan Huy, Thong, Kieu Duy, Dung, Bui Viet, Hoa, Nguyen Minh, Hoa, Truong Khac, Kieu, Nguyen Van, Ngoc, Pham Quy
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2021
• Subjects: Classification; Clustering; Cores; Economic models; Flow; hydraulic flow units; Hydraulics; Learning algorithms; Learning theory; Machine learning; Miocene; Nam Con Son Basin; Neural networks; Permeability; Porosity; Reservoirs; Sediments; Statistical methods; Support vector machines; Well logs; Vietnam
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14227714

The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.